Large-scale multi-session LiDAR mapping is crucial for various applications but still faces significant challenges in data redundancy, memory consumption, and efficiency. This paper presents MS-Mapping, a novel multi-session LiDAR mapping system that incorporates an incremental mapping scheme to enable efficient map assembly in large-scale environments. To address the data redundancy and improve graph optimization efficiency caused by the vast amount of point cloud data, we introduce a real-time keyframe selection method based on the Wasserstein distance. Our approach formulates the LiDAR point cloud keyframe selection problem using a similarity method based on Gaussian mixture models (GMM) and addresses the real-time challenge by employing an incremental voxel update method. To facilitate further research and development in the community, we make our code\footnote{https://github.com/JokerJohn/MS-Mapping} and datasets publicly available.
@article{arxiv.2406.02096,
title = {MS-Mapping: Multi-session LiDAR Mapping with Wasserstein-based Keyframe Selection},
author = {Xiangcheng Hu and Jin Wu and Jianhao Jiao and Wei Zhang and Ping Tan},
journal= {arXiv preprint arXiv:2406.02096},
year = {2024}
}
Comments
3 pages, 2 figures, Accepted by the 40th Anniversary of the IEEE Conference on Robotics and Automation (ICRA@40)